class_docstr: Hugging Face Transformers PostEncoder.
docstr: Initialize the module.
docstr_tok: ["Initialize","the","module","."]
code: def __init__(
      self,
      model_name_or_path: str,
      output_size=256,
  ):
      """Initialize the module."""
      assert check_argument_types()
      super().__init__()
      if not is_transformers_available:
          raise ImportError(
              "`transformers` is not available. Please install it via `pip install"
              " transformers` or `cd /path/to/espnet/tools && . ./activate_python.sh"
              " && ./installers/install_transformers.sh`."
          )
  
      self.model = AutoModel.from_pretrained(model_name_or_path)
      self.tokenizer = AutoTokenizer.from_pretrained(
          model_name_or_path,
          use_fast=True,
      )
      logging.info("Pretrained Transformers model parameters reloaded!")
      self.out_linear = torch.nn.Linear(
          self.model.config.hidden_size, output_size
      )
      self.output_size_dim = output_size
  
file_path: espnet/espnet2/slu/postdecoder/hugging_face_transformers_postdecoder.py
imports: from espnet2.slu.postdecoder.abs_postdecoder import AbsPostDecoder
  import logging
  import torch
  from typeguard import check_argument_types
label_desc: Use image node_id 1 to create a new HuggingFaceTransformersPostDecoder object from inherited base classes: AbsPostDecoder with example: obj = HuggingFaceTransformersPostDecoder(model_name_or_path, output_size)